Python | Pandas Series.all()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.all() function return whether all elements are True, potentially over an axis. It returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Syntax: Series.all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)



Parameter :
axis : Indicate which axis or axes should be reduced.
bool_only : Include only boolean columns.
skipna : Exclude NA/null values.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
**kwargs : Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns : scalar or Series

Example #1: Use Series.all() function to check if all the values in the given series object is True or non-zero.

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# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([34, 5, 13, 32, 4, 15])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

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Output :

Coca Cola    34
Sprite        5
Coke         13
Fanta        32
Dew           4
ThumbsUp     15
dtype: int64

Now we will use Series.all() function to check if all the values in the given series object is True and non-zero.

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# check if all value is True
# or non-zero
result = sr.all()
  
# Print the result
print(result)

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Output :

True

As we can see in the output, the Series.all() function has successfully returned the True indicating that all the values in the given series is True or non-zero.
 
Example #2 : Use Series.all() function to check if all the values in the given series object is True or non-zero.

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# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([51, 10, 24, 18, 1, 84, 12, 10, 5, 24, 0])
  
# Create the Index
# apply yearly frequency
index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

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Output :

2010-12-31 08:45:00    51
2011-12-31 08:45:00    10
2012-12-31 08:45:00    24
2013-12-31 08:45:00    18
2014-12-31 08:45:00     1
2015-12-31 08:45:00    84
2016-12-31 08:45:00    12
2017-12-31 08:45:00    10
2018-12-31 08:45:00     5
2019-12-31 08:45:00    24
2020-12-31 08:45:00     0
Freq: A-DEC, dtype: int64

Now we will use Series.all() function to check if all the values in the given series object is True and non-zero.

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# check if all value is True
# or non-zero
result = sr.all()
  
# Print the result
print(result)

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Output :

False

As we can see in the output, the Series.all() function has successfully returned the False indicating that all the values in the given series is not True or non-zero. One of the values is zero in this series object.



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